Big Data: Where do we start?

According to CSC, “for the majority of small and medium businesses, Big Data is the technology of the future, not the reality they experience today.” Even large Fortune 1000 corporations are just beginning the exploration into Big Data. Cloudera has the majority of the market share in Hadoop, and even they believe they have tapped into less than 10% of the total available market. So, wouldn’t it be better to start where there is the lowest hanging fruit rather than spending millions on new Big Data technologies? Let’s pick up that data lying on the floor all over our enterprises first, gain traction and a measurable ROI, and then dive into Big Data.

Forrester Research reported that most companies are analyzing and using approximately 12% of their existing data. We are all ignoring over 88% of the data we already have. Most of us have financial BI, but not real information to drive the rest of our decisions. PWC completed a study a few years ago and found that a whopping 67% of technology executives, (read that again, Technology Executives) who theoretically have their fingers on all of that data somewhere within the corporation, are making their decisions not on data, but rather on the experience of themselves or others. If that is the case, I wonder what that percentage is for the rest of the executive team.

So, what would be the potential of actually using existing data to derive information-driven business insights? Let’s explore just a few examples of companies who saw the starting point as….leverage what we already have before diving into the deep waters of a data lake.

A large manufacturer, has saved undisclosed millions of dollars in supply chain delays and delivery failures by analyzing how many of the bills of materials had parts with only a single supplier or a small number of suppliers in high risk weather locales (think of the tsunami a few years ago that destroyed many parts factories and drove shortages across the high tech market). A set of hospitals in the Midwest created a portal to manage Sepsis, based upon existing protocols and data, resulting in a significant reduction in mortality rates. Walmart completed analytics on placement of products and increased sales by millions of dollars by placing gaming device extensions and peripherals directly next to the games themselves, rather than by the large boxes of the gaming machines (e.g. by the PS4).

What data do you already have that could help you drive new revenue or reduce operational costs? Instead of chasing the Big Data hype, we should start by picking up the dollars that are lying on the floor. Just pick a use case, one that supports the strategy of the company, analyze it, derive real business value from it…and grow your data analysis footprint. Below are a collection of some simple places to look for that first set of use cases to drive real business value from analytics, and none of these require big data or a data scientist.

Drive New Revenue
a. Who are your top customers (be careful here, I have found that every executive team member has a different definition of our best). What are their        spending habits? What are the products/services that most often sell together? Do our top customers have each of these sets or are there upsell / cross-sell opportunities?
b. Customer seasonality analysis. Do customers buy in a specific season and what can we do to add value off-season?
c. Sales versus service requests, complaints, or other service issue indicators. What is our quotient of sales, especially for our top customers, vs. complaints and requests for service? What can we do to increase the sales end of the quotient and decrease the service end?
d. Touches to conversions. Combine your website data, call center data, and your ERP data to get a view of customer touches, interactions and conversion rates. What can you do to smooth the process and increase conversion rates?</li>

Reduce IT Costs
a. Manage energy consumption through powerful analytics and automation engines like EnergyWise from Cisco. IT consumes 20-80% of the energy in a corporation. Reduce that cost simply, seamlessly, and most importantly…painlessly.
b. Enable self-service analytics. There is almost always an 18 month to 2 year backlog of analytics requests. Enable self-service analytics work benches and reduce the time to market for data insights by more than 50%. See the analytics workbench from Cisco Systems.

3. Increase Employee Retention
a. One Fortune 100 corporation estimates that it costs $100,000 per new employee, including the recruiting process through training and acclimation. What is your cost for a new hire and what is your retention rate? Is there a specific demographic of employees that are running out faster than others? This company’s turnover is highest at 18 months and heavily driven by millennials, making this a very costly corporate activity. After analyzing the turnover rate, most companies are completing simple, fast, surveys of their employees on a regular basis to understand drivers to employee satisfaction and continuously striving to improve their retention rates.

Take a look at your company and find that right fit of the 88% of the data just lying on the floor and pick it up. Identify a use case, beyond the traditional financial reporting, one that drives real and measurable business value. Eventually, you too could be a Netflix or Amazon like, data and insight driven company with a sustainable competitive advantage—information.